Automatic hypertext link typing
Proceedings of the the seventh ACM conference on Hypertext
On the use of information retrieval techniques for the automatic construction of hypertext
Information Processing and Management: an International Journal - Special issue: methods and tools for the automatic construction of hypertext
Building hypertext using information retrieval
Information Processing and Management: an International Journal - Special issue: methods and tools for the automatic construction of hypertext
Automatic construction and management of large open webs
Information Processing and Management: an International Journal - Special issue: methods and tools for the automatic construction of hypertext
Building Hypertext Links By Computing Semantic Similarity
IEEE Transactions on Knowledge and Data Engineering
Lexical cohesion computed by thesaural relations as an indicator of the structure of text
Computational Linguistics
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Traditional Information Retrieval techniques require documents sharing enough number of words in order to build semantic links between them. This kind of techniques is greatly affected by two factors: synonymy (different words have the same meaning) and polysemy (a word has several meanings, also known as ambiguity). Synonymy may result in loss of semantic difference, while polysemy may lead toward wrong semantic links. Stephen J. Green proposes the concept of synset (a set of words having the same or close meaning) and uses a synset method to solve the problem of synonymy and polysemy. Although the synonymy problem can be well solved, the polysemy problem remains, because it is not possible actually to use an entire document as a basis to identify the meaning of a word. In this paper, we propose a concept of context-related semantic set to identify the meaning of a word by considering the relations between the word and its contexts. We believe that this approach can efficiently solve the ambiguity problem and hence support automation of the Web document search and analysis.